A ‘word embeddings’ approach has been widely adopted for machine learning processes. While an extensive research has been carried out during these years to analyze all theoretical underpinnings of algorithms such as word2vec, GloVe or fastText, it is surprising that little has been done, in turn, to solve some of the more complex linguistic issues raised when getting down to business.

Machine learning algorithms require a great amount of numeric data to work properly. Real people, however, do not speak to bots using numbers, they communicate through the natural language. That’s the main reason why chatbot developers need to convert all these words into digits so that those virtual assistants can understand what users are saying. And here is where word embeddings come into play.

Did you know that up to 80% of the data on the Internet is unstructured? All tweets, newspaper articles and any text-heavy information traveling on the network presents an unstructured form, not easily understandable for computers. A mere 20% of all web data is structured and, with only this, not much can be done for data analysis relying on machine learning.

As we have mentioned before in this blog, structured data is invaluable for businesses looking to extract relevant information from text. Whereas the problem used to be how to get enough useful data for the results to be meaningful, the challenge today is how to process the large amounts of it that are available. This task becomes almost impossible to achieve without the right tools because,on top of being vast, the data is most often unstructured. At Bitext, we offer a range of Text Analytics Tools that allow users to structure their raw data to extract the information most relevant to their goals.